Model Developed by
Name: Umair Ali
Contact: +923480233673
Email: uape00@gmail.com
GitHub: https://github.com/1umairali/models

Developed a machine learning-based system for detecting brain tumors from MRI images. The project involved preprocessing image data and extracting relevant features, followed by training and evaluating various machine learning classifiers, including Support Vector Classifier (SVC), Logistic Regression, k-Nearest Neighbors (k-NN), Naives Regression, Random Forest, and Decision Tree. The models were assessed based on accuracy and performance metrics to determine the most effective approach for tumor classification. Demonstrated strong skills in medical image analysis and classical machine learning techniques using Python and scikit-learn.

In [1]:
import numpy as np  # for numeric calculation
import pandas as pd  # for data analysis and manupulation
import matplotlib.pyplot as plt  # for data visualization
import seaborn as sns  # for data visualization
In [2]:
# import dataframe
url = 'https://raw.githubusercontent.com/1umairali/models/main/brain_tumor_detection/brain_dataset.csv'
brain_dataframe = pd.read_csv(url)
brain_dataframe
Out[2]:
Image Class Mean Variance Standard Deviation Entropy Skewness Kurtosis Contrast Energy ASM Homogeneity Dissimilarity Correlation Coarseness
0 Image1 0 6.535339 619.587845 24.891522 0.109059 4.276477 18.900575 98.613971 0.293314 0.086033 0.530941 4.473346 0.981939 7.458341e-155
1 Image2 0 8.749969 805.957634 28.389393 0.266538 3.718116 14.464618 63.858816 0.475051 0.225674 0.651352 3.220072 0.988834 7.458341e-155
2 Image3 1 7.341095 1143.808219 33.820234 0.001467 5.061750 26.479563 81.867206 0.031917 0.001019 0.268275 5.981800 0.978014 7.458341e-155
3 Image4 1 5.958145 959.711985 30.979219 0.001477 5.677977 33.428845 151.229741 0.032024 0.001026 0.243851 7.700919 0.964189 7.458341e-155
4 Image5 0 7.315231 729.540579 27.010009 0.146761 4.283221 19.079108 174.988756 0.343849 0.118232 0.501140 6.834689 0.972789 7.458341e-155
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3757 Image3758 0 21.234512 1208.850174 34.768523 0.063774 2.082079 4.647310 158.437600 0.220666 0.048693 0.487131 5.211739 0.950972 7.458341e-155
3758 Image3759 0 20.435349 1227.151440 35.030721 0.066763 2.144625 4.882034 161.158675 0.225931 0.051045 0.502712 5.083126 0.952749 7.458341e-155
3759 Image3760 0 18.011520 1151.582765 33.934978 0.068396 2.308349 5.579498 167.130118 0.228930 0.052409 0.492269 5.103700 0.952181 7.458341e-155
3760 Image3761 0 13.330429 945.732779 30.752769 0.087872 2.732822 7.757570 223.812932 0.261527 0.068397 0.480064 6.439784 0.940898 7.458341e-155
3761 Image3762 0 6.110138 480.884025 21.929068 0.118171 4.110669 17.538826 239.251388 0.306224 0.093773 0.494333 6.787329 0.938731 7.458341e-155

3762 rows × 15 columns

In [3]:
# Head (first six rows) of brain dataframe
brain_dataframe.head(6)
Out[3]:
Image Class Mean Variance Standard Deviation Entropy Skewness Kurtosis Contrast Energy ASM Homogeneity Dissimilarity Correlation Coarseness
0 Image1 0 6.535339 619.587845 24.891522 0.109059 4.276477 18.900575 98.613971 0.293314 0.086033 0.530941 4.473346 0.981939 7.458341e-155
1 Image2 0 8.749969 805.957634 28.389393 0.266538 3.718116 14.464618 63.858816 0.475051 0.225674 0.651352 3.220072 0.988834 7.458341e-155
2 Image3 1 7.341095 1143.808219 33.820234 0.001467 5.061750 26.479563 81.867206 0.031917 0.001019 0.268275 5.981800 0.978014 7.458341e-155
3 Image4 1 5.958145 959.711985 30.979219 0.001477 5.677977 33.428845 151.229741 0.032024 0.001026 0.243851 7.700919 0.964189 7.458341e-155
4 Image5 0 7.315231 729.540579 27.010009 0.146761 4.283221 19.079108 174.988756 0.343849 0.118232 0.501140 6.834689 0.972789 7.458341e-155
5 Image6 0 7.524109 607.395258 24.645390 0.214086 3.729886 14.471736 105.077882 0.421587 0.177736 0.598169 4.193146 0.976485 7.458341e-155
In [4]:
# Tail (last six rows) of brain dataframe
brain_dataframe.tail(6)
Out[4]:
Image Class Mean Variance Standard Deviation Entropy Skewness Kurtosis Contrast Energy ASM Homogeneity Dissimilarity Correlation Coarseness
3756 Image3757 0 20.976822 1144.456066 33.829810 0.062252 2.106235 4.798339 166.395916 0.217934 0.047495 0.488449 5.193088 0.948808 7.458341e-155
3757 Image3758 0 21.234512 1208.850174 34.768523 0.063774 2.082079 4.647310 158.437600 0.220666 0.048693 0.487131 5.211739 0.950972 7.458341e-155
3758 Image3759 0 20.435349 1227.151440 35.030721 0.066763 2.144625 4.882034 161.158675 0.225931 0.051045 0.502712 5.083126 0.952749 7.458341e-155
3759 Image3760 0 18.011520 1151.582765 33.934978 0.068396 2.308349 5.579498 167.130118 0.228930 0.052409 0.492269 5.103700 0.952181 7.458341e-155
3760 Image3761 0 13.330429 945.732779 30.752769 0.087872 2.732822 7.757570 223.812932 0.261527 0.068397 0.480064 6.439784 0.940898 7.458341e-155
3761 Image3762 0 6.110138 480.884025 21.929068 0.118171 4.110669 17.538826 239.251388 0.306224 0.093773 0.494333 6.787329 0.938731 7.458341e-155
In [5]:
# Information of brain dataframe
brain_dataframe.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3762 entries, 0 to 3761
Data columns (total 15 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Image               3762 non-null   object 
 1   Class               3762 non-null   int64  
 2   Mean                3762 non-null   float64
 3   Variance            3762 non-null   float64
 4   Standard Deviation  3762 non-null   float64
 5   Entropy             3762 non-null   float64
 6   Skewness            3762 non-null   float64
 7   Kurtosis            3762 non-null   float64
 8   Contrast            3762 non-null   float64
 9   Energy              3762 non-null   float64
 10  ASM                 3762 non-null   float64
 11  Homogeneity         3762 non-null   float64
 12  Dissimilarity       3762 non-null   float64
 13  Correlation         3762 non-null   float64
 14  Coarseness          3762 non-null   float64
dtypes: float64(13), int64(1), object(1)
memory usage: 441.0+ KB
In [6]:
# show image columns
print(brain_dataframe['Image'])

0          Image1
1          Image2
2          Image3
3          Image4
4          Image5
          ...    
3757    Image3758
3758    Image3759
3759    Image3760
3760    Image3761
3761    Image3762
Name: Image, Length: 3762, dtype: object

image column is object dtype. contains only images name. if we drop that column it will not impact dataframe or result

In [7]:
# drop Image column.
brain_df2 = brain_dataframe.drop(['Image'], axis = 1)
brain_df2
Out[7]:
Class Mean Variance Standard Deviation Entropy Skewness Kurtosis Contrast Energy ASM Homogeneity Dissimilarity Correlation Coarseness
0 0 6.535339 619.587845 24.891522 0.109059 4.276477 18.900575 98.613971 0.293314 0.086033 0.530941 4.473346 0.981939 7.458341e-155
1 0 8.749969 805.957634 28.389393 0.266538 3.718116 14.464618 63.858816 0.475051 0.225674 0.651352 3.220072 0.988834 7.458341e-155
2 1 7.341095 1143.808219 33.820234 0.001467 5.061750 26.479563 81.867206 0.031917 0.001019 0.268275 5.981800 0.978014 7.458341e-155
3 1 5.958145 959.711985 30.979219 0.001477 5.677977 33.428845 151.229741 0.032024 0.001026 0.243851 7.700919 0.964189 7.458341e-155
4 0 7.315231 729.540579 27.010009 0.146761 4.283221 19.079108 174.988756 0.343849 0.118232 0.501140 6.834689 0.972789 7.458341e-155
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3757 0 21.234512 1208.850174 34.768523 0.063774 2.082079 4.647310 158.437600 0.220666 0.048693 0.487131 5.211739 0.950972 7.458341e-155
3758 0 20.435349 1227.151440 35.030721 0.066763 2.144625 4.882034 161.158675 0.225931 0.051045 0.502712 5.083126 0.952749 7.458341e-155
3759 0 18.011520 1151.582765 33.934978 0.068396 2.308349 5.579498 167.130118 0.228930 0.052409 0.492269 5.103700 0.952181 7.458341e-155
3760 0 13.330429 945.732779 30.752769 0.087872 2.732822 7.757570 223.812932 0.261527 0.068397 0.480064 6.439784 0.940898 7.458341e-155
3761 0 6.110138 480.884025 21.929068 0.118171 4.110669 17.538826 239.251388 0.306224 0.093773 0.494333 6.787329 0.938731 7.458341e-155

3762 rows × 14 columns

In [8]:
# Numerical distribution of data
brain_df2.describe()
Out[8]:
Class Mean Variance Standard Deviation Entropy Skewness Kurtosis Contrast Energy ASM Homogeneity Dissimilarity Correlation Coarseness
count 3762.000000 3762.000000 3762.000000 3762.000000 3762.000000 3762.000000 3762.000000 3762.000000 3762.000000 3762.000000 3762.000000 3762.000000 3762.000000 3.762000e+03
mean 0.447368 9.488890 711.101063 25.182271 0.073603 4.102727 24.389071 127.961459 0.204705 0.058632 0.479252 4.698498 0.955767 7.458341e-155
std 0.497288 5.728022 467.466896 8.773526 0.070269 2.560940 56.434747 109.499601 0.129352 0.058300 0.127929 1.850173 0.026157 0.000000e+00
min 0.000000 0.078659 3.145628 1.773592 0.000882 1.886014 3.942402 3.194733 0.024731 0.000612 0.105490 0.681121 0.549426 7.458341e-155
25% 0.000000 4.982395 363.225459 19.058475 0.006856 2.620203 7.252852 72.125208 0.069617 0.004847 0.364973 3.412363 0.947138 7.458341e-155
50% 0.000000 8.477531 622.580417 24.951560 0.066628 3.422210 12.359088 106.737418 0.225496 0.050849 0.512551 4.482404 0.961610 7.458341e-155
75% 1.000000 13.212723 966.954319 31.095889 0.113284 4.651737 22.640304 161.059006 0.298901 0.089342 0.575557 5.723821 0.971355 7.458341e-155
max 1.000000 33.239975 2910.581879 53.949809 0.394539 36.931294 1371.640060 3382.574163 0.589682 0.347725 0.810921 27.827751 0.989972 7.458341e-155
In [9]:
# check sum of null values in each columns
brain_df2.isnull().sum()
Out[9]:
Class                 0
Mean                  0
Variance              0
Standard Deviation    0
Entropy               0
Skewness              0
Kurtosis              0
Contrast              0
Energy                0
ASM                   0
Homogeneity           0
Dissimilarity         0
Correlation           0
Coarseness            0
dtype: int64

Data Visualization¶

In [10]:
# Pairplot of brain dataframe
sns.pairplot(brain_df2, hue = 'Class')

C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
Out[10]:
<seaborn.axisgrid.PairGrid at 0x21f83af1dd0>
No description has been provided for this image
In [11]:
# Count the class columns
# 0 = Non tumor / no cancer
# 1 = Tumor / has cancer
sns.countplot(x=brain_df2["Class"])
Out[11]:
<Axes: xlabel='Class', ylabel='count'>
No description has been provided for this image

Heatmap¶

In [12]:
# heatmap of DataFrame
plt.figure(figsize=(16,9))
sns.heatmap(brain_df2)
Out[12]:
<Axes: >
No description has been provided for this image

Heatmap of a correlation Matrix¶

In [13]:
# correlation matrix
brain_df2.corr()
Out[13]:
Class Mean Variance Standard Deviation Entropy Skewness Kurtosis Contrast Energy ASM Homogeneity Dissimilarity Correlation Coarseness
Class 1.000000 -0.095729 0.308818 0.285568 -0.778180 0.402644 0.239844 0.212643 -0.862413 -0.758255 -0.847529 0.556319 -0.108601 NaN
Mean -0.095729 1.000000 0.783027 0.790984 -0.099729 -0.601593 -0.358163 -0.050974 -0.014863 -0.109393 0.095556 -0.113864 0.293693 NaN
Variance 0.308818 0.783027 1.000000 0.975699 -0.344432 -0.347399 -0.248312 0.135494 -0.335470 -0.341061 -0.290527 0.235487 0.288037 NaN
Standard Deviation 0.285568 0.790984 0.975699 1.000000 -0.345127 -0.425428 -0.329798 0.117981 -0.331103 -0.342530 -0.288801 0.224773 0.354161 NaN
Entropy -0.778180 -0.099729 -0.344432 -0.345127 1.000000 -0.222222 -0.140125 -0.140769 0.971260 0.999213 0.852019 -0.502363 0.122080 NaN
Skewness 0.402644 -0.601593 -0.347399 -0.425428 -0.222222 1.000000 0.899713 0.349856 -0.295413 -0.209289 -0.470054 0.511931 -0.570919 NaN
Kurtosis 0.239844 -0.358163 -0.248312 -0.329798 -0.140125 0.899713 1.000000 0.296664 -0.172454 -0.133741 -0.307314 0.375939 -0.589211 NaN
Contrast 0.212643 -0.050974 0.135494 0.117981 -0.140769 0.349856 0.296664 1.000000 -0.130708 -0.139276 -0.270119 0.761497 -0.427443 NaN
Energy -0.862413 -0.014863 -0.335470 -0.331103 0.971260 -0.295413 -0.172454 -0.130708 1.000000 0.961628 0.915988 -0.545774 0.123680 NaN
ASM -0.758255 -0.109393 -0.341061 -0.342530 0.999213 -0.209289 -0.133741 -0.139276 0.961628 1.000000 0.837139 -0.491813 0.121054 NaN
Homogeneity -0.847529 0.095556 -0.290527 -0.288801 0.852019 -0.470054 -0.307314 -0.270119 0.915988 0.837139 1.000000 -0.746675 0.198639 NaN
Dissimilarity 0.556319 -0.113864 0.235487 0.224773 -0.502363 0.511931 0.375939 0.761497 -0.545774 -0.491813 -0.746675 1.000000 -0.393013 NaN
Correlation -0.108601 0.293693 0.288037 0.354161 0.122080 -0.570919 -0.589211 -0.427443 0.123680 0.121054 0.198639 -0.393013 1.000000 NaN
Coarseness NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

we checked in information 'Coarseness' column is float datatype. contains 0 values which is not empty....... but in corelation matrix it contain NaN values...... we have to remove that column. it will not impact the result...... lets confirm in Heatmap and Barplot of Correlation Matrix

In [14]:
# Heatmap of Correlation matrix of Brain DataFrame
plt.figure(figsize=(10,8))
sns.heatmap(brain_df2.corr(), annot = True, cmap ='coolwarm', linewidths=2)

C:\Users\Umair Ali\anaconda3\Lib\site-packages\seaborn\matrix.py:260: FutureWarning: Format strings passed to MaskedConstant are ignored, but in future may error or produce different behavior
  annotation = ("{:" + self.fmt + "}").format(val)
Out[14]:
<Axes: >
No description has been provided for this image
In [15]:
# drop coarseness column from brain_df2
brain_df3 = brain_df2.drop(['Coarseness'], axis=1)

Split Dataframe in Train and Test¶

In [16]:
# drop dependent (Class) column, it will assign to y
X = brain_df3.drop(['Class'], axis = 1)
X.head(6)
Out[16]:
Mean Variance Standard Deviation Entropy Skewness Kurtosis Contrast Energy ASM Homogeneity Dissimilarity Correlation
0 6.535339 619.587845 24.891522 0.109059 4.276477 18.900575 98.613971 0.293314 0.086033 0.530941 4.473346 0.981939
1 8.749969 805.957634 28.389393 0.266538 3.718116 14.464618 63.858816 0.475051 0.225674 0.651352 3.220072 0.988834
2 7.341095 1143.808219 33.820234 0.001467 5.061750 26.479563 81.867206 0.031917 0.001019 0.268275 5.981800 0.978014
3 5.958145 959.711985 30.979219 0.001477 5.677977 33.428845 151.229741 0.032024 0.001026 0.243851 7.700919 0.964189
4 7.315231 729.540579 27.010009 0.146761 4.283221 19.079108 174.988756 0.343849 0.118232 0.501140 6.834689 0.972789
5 7.524109 607.395258 24.645390 0.214086 3.729886 14.471736 105.077882 0.421587 0.177736 0.598169 4.193146 0.976485
In [17]:
# output variable
# assign Class column to y
y = brain_df3['Class']
y.head(6)
Out[17]:
0    0
1    0
2    1
3    1
4    0
5    0
Name: Class, dtype: int64
In [18]:
# split dataset into train and test
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state= 5)
In [19]:
X_train
Out[19]:
Mean Variance Standard Deviation Entropy Skewness Kurtosis Contrast Energy ASM Homogeneity Dissimilarity Correlation
2021 7.885529 681.803866 26.111374 0.133134 3.806777 14.939540 277.900762 0.326207 0.106411 0.546301 6.445886 0.944215
1286 6.639282 207.782336 14.414657 0.102582 2.607407 7.151179 33.038741 0.282621 0.079875 0.638838 2.064030 0.959070
1106 3.186020 314.159477 17.724544 0.179837 6.354288 43.889536 165.700313 0.383583 0.147136 0.569095 4.329943 0.969997
3688 7.752167 850.780349 29.168139 0.000954 4.667157 23.797444 162.713111 0.025726 0.000662 0.255565 7.630112 0.966914
1781 15.907272 859.296845 29.313765 0.093459 2.257804 5.399814 111.219504 0.269897 0.072845 0.543017 3.945097 0.960212
... ... ... ... ... ... ... ... ... ... ... ... ...
3190 19.755035 1511.266702 38.875014 0.098117 2.359856 5.823204 155.330111 0.276962 0.076708 0.564342 4.108485 0.970138
3046 17.384506 1151.899226 33.939641 0.007915 2.516995 6.645917 119.718675 0.075044 0.005632 0.381815 5.310392 0.944067
1725 9.873734 647.095405 25.438070 0.203971 2.936003 8.936402 116.379595 0.410201 0.168265 0.653499 3.168139 0.965933
2254 4.993439 837.608629 28.941469 0.001184 6.201616 39.682251 98.674332 0.028656 0.000821 0.218662 6.251872 0.960241
2915 14.946762 1063.536983 32.611915 0.144926 2.589608 7.070669 140.031810 0.341421 0.116569 0.579450 4.088308 0.968672

3009 rows × 12 columns

In [20]:
X_test
Out[20]:
Mean Variance Standard Deviation Entropy Skewness Kurtosis Contrast Energy ASM Homogeneity Dissimilarity Correlation
1829 14.244751 677.906461 26.036637 0.064730 2.245463 5.280704 68.194040 0.221802 0.049196 0.573020 3.171980 0.965574
142 6.439590 711.402252 26.672125 0.003683 4.945783 26.258025 191.833333 0.050998 0.002601 0.252045 8.161609 0.962899
2934 18.273666 960.554970 30.992821 0.053945 2.113554 4.673078 135.033939 0.201307 0.040524 0.550705 3.961433 0.943238
1648 8.556091 557.147920 23.603981 0.187408 3.215624 10.791892 87.087468 0.392090 0.153735 0.626582 3.201900 0.973064
1178 6.661285 761.907895 27.602679 0.004002 4.637791 22.559937 81.489763 0.052887 0.002797 0.370518 4.495690 0.969339
... ... ... ... ... ... ... ... ... ... ... ... ...
3044 2.566971 254.252713 15.945304 0.001956 6.762827 47.224837 43.256424 0.036846 0.001358 0.317558 4.147622 0.983775
2743 11.161804 736.299788 27.134845 0.071535 2.928248 8.891376 147.695042 0.234516 0.054998 0.510246 5.120198 0.954483
3413 7.838409 300.574737 17.337091 0.134217 2.797981 8.655661 51.790533 0.327457 0.107228 0.616866 2.620355 0.960541
1619 4.232285 153.173766 12.376339 0.095976 3.288243 11.029013 35.134788 0.271702 0.073822 0.639419 2.224797 0.956256
3220 8.853958 573.067789 23.938834 0.006357 3.228923 10.874134 90.170569 0.067020 0.004492 0.391093 4.633593 0.939068

753 rows × 12 columns

In [21]:
y_train
Out[21]:
2021    0
1286    0
1106    1
3688    1
1781    0
       ..
3190    0
3046    1
1725    0
2254    1
2915    0
Name: Class, Length: 3009, dtype: int64
In [22]:
y_test
Out[22]:
1829    0
142     1
2934    0
1648    0
1178    1
       ..
3044    1
2743    0
3413    0
1619    0
3220    1
Name: Class, Length: 753, dtype: int64

Feature Scaling¶

In [23]:
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train_sc = sc.fit_transform(X_train)
X_test_sc = sc.transform(X_test)

ML Model Building¶

In [24]:
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score

Support Vector Classifier¶

Train with original data¶
In [25]:
from sklearn.svm import SVC
svc_classifier = SVC()
svc_classifier.fit(X_train, y_train)
y_pred_svc = svc_classifier.predict(X_test)
accuracy_score(y_test, y_pred_svc)
Out[25]:
0.7861885790172642
Train with scaled data¶
In [26]:
svc_classifier2 = SVC()
svc_classifier2.fit(X_train_sc, y_train)
y_pred_svc_sc = svc_classifier2.predict(X_test_sc)
accuracy_score(y_test, y_pred_svc_sc)
Out[26]:
0.9774236387782205

Logistic Regression¶

Train with original data¶
In [27]:
from sklearn.linear_model import LogisticRegression
lr_classifier = LogisticRegression(random_state=51, penalty = 'l2')
lr_classifier.fit(X_train, y_train)
y_pred_lr = lr_classifier.predict(X_test)
accuracy_score(y_test, y_pred_lr)

C:\Users\Umair Ali\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
Out[27]:
0.9096945551128818
Train with scaled data¶
In [28]:
lr_classifier2 = LogisticRegression(random_state=51, penalty = 'l2')
lr_classifier2.fit(X_train_sc, y_train)
y_pred_lr_sc = lr_classifier2.predict(X_test_sc)
accuracy_score(y_test, y_pred_lr_sc)
Out[28]:
0.9787516600265604

KNN - K-Nearesr Neighbor Classifier¶

Train with original data¶
In [29]:
from sklearn.neighbors import KNeighborsClassifier
knn_classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
knn_classifier.fit(X_train, y_train)
y_pred_knn = knn_classifier.predict(X_test)
accuracy_score(y_test, y_pred_knn)
Out[29]:
0.8061088977423638
Train with scaled data¶
In [30]:
knn_classifier2 = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
knn_classifier2.fit(X_train_sc, y_train)
y_pred_knn_sc = knn_classifier2.predict(X_test_sc)
accuracy_score(y_test, y_pred_knn_sc)
Out[30]:
0.9774236387782205

Naive Bayes Classifier¶

Train with original data¶
In [31]:
from sklearn.naive_bayes import GaussianNB
nb_classifier = GaussianNB()
nb_classifier.fit(X_train, y_train)
y_pred_nb = nb_classifier.predict(X_test)
accuracy_score(y_test, y_pred_nb)
Out[31]:
0.953519256308101
Train with scaled data¶
In [32]:
nb_classifier2 = GaussianNB()
nb_classifier2.fit(X_train_sc, y_train)
y_pred_nb_sc = nb_classifier2.predict(X_test_sc)
accuracy_score(y_test, y_pred_nb_sc)
Out[32]:
0.9548472775564409

Decision Tree Classifier¶

Train with original data¶
In [33]:
from sklearn.tree import DecisionTreeClassifier
dt_classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 51)
dt_classifier.fit(X_train, y_train)
y_pred_dt = dt_classifier.predict(X_test)
accuracy_score(y_test, y_pred_dt)
Out[33]:
0.9814077025232404
Train with scaled data¶
In [34]:
dt_classifier2 = DecisionTreeClassifier(criterion = 'entropy', random_state = 51)
dt_classifier2.fit(X_train_sc, y_train)
y_pred_dt_sc = dt_classifier2.predict(X_test_sc)
accuracy_score(y_test, y_pred_dt_sc)
Out[34]:
0.9814077025232404

Random Forest Classifier¶

Train with original data¶
In [35]:
from sklearn.ensemble import RandomForestClassifier
rf_classifier = RandomForestClassifier(n_estimators = 20, criterion = 'entropy', random_state = 51)
rf_classifier.fit(X_train, y_train)
y_pred_rf = rf_classifier.predict(X_test)
accuracy_score(y_test, y_pred_rf)
Out[35]:
0.9827357237715804
Train with scaled data¶
In [36]:
rf_classifier2 = RandomForestClassifier(n_estimators = 20, criterion = 'entropy', random_state = 51)
rf_classifier2.fit(X_train_sc, y_train)
y_pred_rf_sc = rf_classifier2.predict(X_test_sc)
accuracy_score(y_test, y_pred_rf_sc)
Out[36]:
0.9827357237715804

Confusion Matrix¶

In [37]:
cm = confusion_matrix(y_test, y_pred_rf_sc)
plt.title('Heatmap of Confusion Matrix', fontsize = 15)
sns.heatmap(cm, annot = True)
plt.show()
No description has been provided for this image

Classification Report Of model¶

In [38]:
print(classification_report(y_test, y_pred_rf_sc))

              precision    recall  f1-score   support

           0       0.98      0.99      0.98       411
           1       0.99      0.97      0.98       342

    accuracy                           0.98       753
   macro avg       0.98      0.98      0.98       753
weighted avg       0.98      0.98      0.98       753

Cross-validation of the ML model¶

In [39]:
# Cross validation
from sklearn.model_selection import cross_val_score
cross_validation = cross_val_score(estimator = rf_classifier2, X = X_train_sc,y = y_train, cv = 10)
print("Cross validation accuracy of SVC model = ", cross_validation)
print("\nCross validation mean accuracy of SVC model = ", cross_validation.mean())

Cross validation accuracy of SVC model =  [0.98671096 0.99335548 0.99003322 0.97009967 0.9833887  0.97674419
 0.99335548 0.99003322 0.99335548 0.99666667]

Cross validation mean accuracy of SVC model =  0.9873743078626799

Test Model¶

In [45]:
# we have one patient data
patient1 = [17.99,10.38,122.8,1001.0,0.1184,0.2776,0.3001,
            0.1471,0.2419,0.07871,1.095,0.9053]
In [41]:
# convert list into array and scale
patient1_sc = sc.transform(np.array([patient1]))
patient1_sc

C:\Users\Umair Ali\anaconda3\Lib\site-packages\sklearn\base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names
  warnings.warn(
Out[41]:
array([[ 1.49256979e+00, -1.49381108e+00,  1.11029758e+01,
         1.40960224e+04, -1.54791906e+00, -4.16334494e-01,
        -1.13805522e+00, -4.48064734e-01,  3.09915646e+00,
        -3.10934480e+00, -1.93429813e+00, -1.84574993e+00]])
In [42]:
# predict patient1_sc scale data
# zero mean malignant patient has cancer
predict = rf_classifier2.predict(patient1_sc)
predict
Out[42]:
array([0], dtype=int64)
In [43]:
# write if else statement to print result in clear format
if predict[0] == 0:
    print ('Patient has *** NO *** Tumor / Cancer')
else:
    print ('Patient *** HAS *** Tumor / Cancer')

Patient has *** NO *** Tumor / Cancer
In [44]:
# confusion matrix
print('Confusion matrix of Random Forest model: \n',confusion_matrix(y_test, y_pred_rf_sc),'\n')

# show the accuracy
print('Accuracy of Random Forest model = ',accuracy_score(y_test, y_pred_rf_sc))

Confusion matrix of Random Forest model: 
 [[408   3]
 [ 10 332]] 

Accuracy of Random Forest model =  0.9827357237715804